86,544 research outputs found
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Cloud computing systems promise to offer subscription-oriented,
enterprise-quality computing services to users worldwide. With the increased
demand for delivering services to a large number of users, they need to offer
differentiated services to users and meet their quality expectations. Existing
resource management systems in data centers are yet to support Service Level
Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to
realize cloud computing and utility computing. In addition, no work has been
done to collectively incorporate customer-driven service management,
computational risk management, and autonomic resource management into a
market-based resource management system to target the rapidly changing
enterprise requirements of Cloud computing. This paper presents vision,
challenges, and architectural elements of SLA-oriented resource management. The
proposed architecture supports integration of marketbased provisioning policies
and virtualisation technologies for flexible allocation of resources to
applications. The performance results obtained from our working prototype
system shows the feasibility and effectiveness of SLA-based resource
provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE
International Conference on Cloud and Service Computing (CSC 2011, IEEE
Press, USA), Hong Kong, China, December 12-14, 201
Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use
The creation of Domain Specific Languages(DSL) counts as one of the main
goals in the field of Model-Driven Software Engineering (MDSE). The main
purpose of these DSLs is to facilitate the manipulation of domain specific
concepts, by providing developers with specific tools for their domain of
expertise. A natural approach to create DSLs is to reuse existing modeling
standards and tools. In this area, the Eclipse Modeling Framework (EMF) has
rapidly become the defacto standard in the MDSE for building Domain Specific
Languages (DSL) and tools based on generative techniques. However, the use of
EMF generated tools in domains like Internet of Things (IoT), Cloud Computing
or Models@Runtime reaches several limitations. In this paper, we identify
several properties the generated tools must comply with to be usable in other
domains than desktop-based software systems. We then challenge EMF on these
properties and describe our approach to overcome the limitations. Our approach,
implemented in the Kevoree Modeling Framework (KMF), is finally evaluated
according to the identified properties and compared to EMF.Comment: ISBN 978-2-87971-131-7; N° TR-SnT-2014-11 (2014
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
A Factor Framework for Experimental Design for Performance Evaluation of Commercial Cloud Services
Given the diversity of commercial Cloud services, performance evaluations of
candidate services would be crucial and beneficial for both service customers
(e.g. cost-benefit analysis) and providers (e.g. direction of service
improvement). Before an evaluation implementation, the selection of suitable
factors (also called parameters or variables) plays a prerequisite role in
designing evaluation experiments. However, there seems a lack of systematic
approaches to factor selection for Cloud services performance evaluation. In
other words, evaluators randomly and intuitively concerned experimental factors
in most of the existing evaluation studies. Based on our previous taxonomy and
modeling work, this paper proposes a factor framework for experimental design
for performance evaluation of commercial Cloud services. This framework
capsules the state-of-the-practice of performance evaluation factors that
people currently take into account in the Cloud Computing domain, and in turn
can help facilitate designing new experiments for evaluating Cloud services.Comment: 8 pages, Proceedings of the 4th International Conference on Cloud
Computing Technology and Science (CloudCom 2012), pp. 169-176, Taipei,
Taiwan, December 03-06, 201
Addressing the Node Discovery Problem in Fog Computing
In recent years, the Internet of Things (IoT) has gained a lot of attention due to connecting various sensor devices with the cloud, in order to enable smart applications such as: smart traffic management, smart houses, and smart grids, among others. Due to the growing popularity of the IoT, the number of Internet-connected devices has increased significantly. As a result, these devices generate a huge amount of network traffic which may lead to bottlenecks, and eventually increase the communication latency with the cloud. To cope with such issues, a new computing paradigm has emerged, namely: fog computing. Fog computing enables computing that spans from the cloud to the edge of the network in order to distribute the computations of the IoT data, and to reduce the communication latency. However, fog computing is still in its infancy, and there are still related open problems. In this paper, we focus on the node discovery problem, i.e., how to add new compute nodes to a fog computing system. Moreover, we discuss how addressing this problem can have a positive impact on various aspects of fog computing, such as fault tolerance, resource heterogeneity, proximity awareness, and scalability. Finally, based on the experimental results that we produce by simulating various distributed compute nodes, we show how addressing the node discovery problem can improve the fault tolerance of a fog computing system
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